CDNA microarray

There are many protocols and different types of platforms available, e.g. GeneChips from Affymetrix, Illumina Bead Arrays, Arrays from Agilent, Applied Biosystems, GE Healthcare, customized spotted cDNA microarrays etc. the basic procedure for a large-scale measurement of gene expression involves the preparation of total or mRNA from the biological sample(s) under investigation (e.g. candidate tissue) and the hybridization of copied labelled RNA or cDNA to the DNA elements on the array surface (Fig. 1). 

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Unlike other methods currently employed for quantitative transcript measurements, including cDNA microarrays and real-time RT-PCR, competitive RT-PCR is amenable to quality control, which is critical for clinical diagnostic and pharmaceutical industry applications. Furthermore, microarray approaches are limited to generating snap-shot like profiles, but they do not control for differences in hybridization efficiencies of different gene probes with their corresponding cDNAs. That is, cross comparisons are relative and not absolute. Real-time PCR has gained acceptance recently largely due to the reduced cost associated... 

This section provides only a basic outline of concepts surrounding cDNA microarrays. Eor further reading a number of excellent detailed descriptions of this technology are recommended (Geschwind, 2000 Luo and Geschwind, 2001 Marcotte et al., 2001 Li et al., 2002). 

Eor every microarray experiment the first and most important step is experimental design. A badly designed experiment can render microarray data unsuitable for addressing the experimental questions or worse, lead the investigator to draw false conclusions. Furthermore, failed microarray experiments can be very costly both in terms of resources and time. There are many issues that must be addressed when planning a cDNA microarray experiment, some intuitive, others requiring considerable thought. 

The growing commercial availability and relative affordability of cDNA microarrays combined with well-defined protocols for hybridization has made functional genomics a reality for many laboratories. However microarray experiments produce massive quantities of gene expression and functional genomics data, the analysis of which is complicated and involves many steps, each requiring careful consideration. 

The typical cDNA microarray study can be described in nine steps (1) establishing an appropriate experimental design (2) isolation and conversion of mRNA to labeled cDNA (3) hybridization of labeled cDNA to the microarray slide (4) image acquisition, (5) data storage, (6) normalization (7) statistical analysis (8) data mining and (9) validation of the results. Each of these steps is multifaceted and the introduction of error at any point in the process can lead to costly loss of data. The following section describes the steps followed in experimental design. 

The steps involved in the image acquisition process for cDNA microarrays have been reviewed and a number of image analysis programs are available to mediate this process. The process can be described in four basic steps (1) scanning, (2) spot recognition, (3) segmentation (4) intensity measurement, and (5) ratio calculation (Leung and Cavalieri, 2003). 

Normalization of cDNA microarray data is a very important step in the process of data analysis. With current technology, systematic hias is unavoidable and must he dealt with in a sensible manner. Furthermore, normalization methods need to be consistently apphed to all raw data. Using different normalization methods on different datasets may introduce bias and thereby decrease the validity of the data. Normahzed data should be free of systematic bias and should thereby provide a truer representation of the biological variance. Furthermore, normahzed data increases the validity of shde to shde comparisons. 

Current practice in microarray experimentation suggests that a balance design with adequate replication be used. Good experimental design and execution will produce data that minimize technical variance, allowing the statistical analyses to evaluate biological variance more effectively Still, the nature of the data requires that an estimate of the FDR be included in the statistical analysis. This enables the researcher to assess the reliability/validity of the results of the statistical analysis. As discussed earlier, cDNA microarray... 

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